Publications

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Current research topics include distributed learning, random feature extraction (e.g. Echo State Networks), and adaptive audio processing. Below you can find an updated list of my publications, sorted by type. When available, you can find the pointers to the respective presentation, source code, and/or the full text published here and on ResearchGate (RG).

For the full texts, copyright and all rights therein are retained by authors or by the copyright holders. All persons accessing this information are expected to adhere to the terms and constraints invoked by each case’s copyright.

Jump to: Ph.D. Thesis – Preprints – JournalsConferencesChapters

Ph.D. Thesis

Scardapane, S., (2016). Distributed supervised learning using neural networks. Ph.D. Dissertation, Sapienza University of Rome, Italy, May 2016.
[PDF] [RG]

Preprints

[P3] Scardapane, S., Chen, J., & Richard, C. (2017). Adaptation and learning over networks for nonlinear system modeling. arXiv preprint arXiv:1605.05509.
[To be published as a chapter in `Adaptive Learning Methods for Nonlinear System Modeling‘, Elsevier Publishing, Eds. D. Comminiello and J.C. Principe (2018)]

[P2] Scardapane, S., Scarpiniti, M., Comminiello, D. & Uncini, A. (2016). Learning activation functions from data using cubic spline interpolation. arXiv preprint arXiv:1605.05509.
[CODE]

[P1] Scarpiniti, M., Scardapane, S., Comminiello, D., Parisi, R. & Uncini, A. (2016). Effective Blind Source Separation Based on the Adam Algorithm. arXiv preprint arXiv:1605.07833 [presented at WIRN 2016, to be published on Springer].

Journals

[J16] Scardapane, S. & Di Lorenzo, P. (2017). A Framework for Parallel and Distributed Training of Neural Networks. Neural Networks, 91, 42–54.
[arXiv] [PDF] [RG] [CODE]

[J15] Scardapane, S., Butcher, J., Bianchi, F.M., & Malik, Z. (2017). Advances in Biologically Inspired Reservoir Computing [Guest Editorial]. Cognitive Computation, in press.
[PDF] [RG]

[J14] Scardapane, S., Comminiello, D., Hussain, A. & Uncini, A. (2017). Group Sparse Regularization for Deep Neural Networks. Neurocomputing, 241, pp. 81-89.
[arXiv] [PDF] [RG] [CODE]

[J13] Scardapane, S. & Wang, D. (2017). Randomness in neural networks: an overview. WIREs Data Mining and Knowledge Discovery, 7(2), pp. 1-18.
[PDF] [RG]

[J12] Fierimonte, R., Scardapane, S., Uncini, A. & Panella, M. (2016). Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix CompletionIEEE Transactions on Neural Networks and Learning Systems, in press.
[PDF] [RG] [CODE]

[J11] Scardapane, S. & Uncini, A. (2016). Semi-supervised Echo State Networks for Audio ClassificationCognitive Computation, 9(1), pp. 125-135.
[PDF] [RG]

[J10] Scardapane, S., Panella, M., Comminiello, D., Hussain, A. & Uncini, A. (2017). Distributed reservoir computing with sparse readoutsIEEE Computational Intelligence Magazine, 11(4), pp. 59-70.
[PDF] [RG]

[J9] Scardapane, S., Fierimonte, R, Di Lorenzo, P., Panella, M. & Uncini, A. (2016). Distributed semi-supervised support vector machinesNeural Networks, 80, pp. 43-52.
[PDF] [RG] [CODE]

[J8] Scardapane, S., Wang, D., & Panella, M. (2016). A decentralized training algorithm for Echo State Networks in distributed big data applications. Neural Networks, 78, pp. 65-74.
[PDF] [RG] [CODE]

[J7] Bianchi, F.M., Scardapane, S., Rizzi, A., Uncini, A., & Sadeghian, A. (2016). Granular Computing Techniques for Classification and Semantic Characterization of Structured Data. Cognitive Computation, 8(3), pp. 442-461.
[PDF] [RG]

[J6] Bianchi, F. M., Scardapane, S., Uncini, A., Rizzi, A., Sadeghian, A. (2015). Prediction of telephone calls load using Echo State Network with exogenous variables. Neural Networks, 71, pp. 204-213.
[PDF] [RG]

[J5] Scardapane, S., Comminiello, D., Scarpiniti, M., & Uncini, A. (2016). A Semi-supervised Random Vector Functional-Link Network based on the Transductive Framework. Information Sciences, 364-365, pp. 156–166.
[PDF] [RG] [CODE]

[J4] Scardapane, S., Scarpiniti, M., Bucciarelli, M., Colone, F., Mansueto, M. V., & Parisi, R. (2015). Microphone Array Based Classification for Security Monitoring in Unstructured EnvironmentsAEU-International Journal of Electronics and Communications, 69(11), pp. 1715-1723.
[PDF] [RG]

[J3] Comminiello, D., Scarpiniti, M., Scardapane, S., Parisi, R. & Uncini, A. (2015). Improving nonlinear modeling capabilities of functional link adaptive filtersNeural Networks, 69, pp. 51-59.
[PDF] [RG]

[J2] Scardapane, S., Wang, D., Panella, M. & Uncini, A. (2015). Distributed Learning for Random Vector Functional-Link NetworksInformation Sciences, 301, pp. 271-284.
[PDF] [RG] [CODE]

[J1] Scardapane, S., Comminiello, D., Scarpiniti, M. & Uncini, A. (2015). Online Sequential Extreme Learning Machine With Kernels. IEEE Transactions on Neural Networks and Learning Systems, 26(9), pp. 2214-2200.
[PDF] [RG] [CODE]

Conference Proceedings

[C14] Di Lorenzo, P. & Scardapane, S.. (2016). Parallel and Distributed Training of Neural Networks via Successive Convex Approximation. In 2016 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), (pp. 1-6). IEEE.
[PDF] [RG] [PRESENTATION] [CODE]

[C13] Scardapane, S., Altilio, R., Panella, M. & Uncini, A. (2016). Distributed Spectral Clustering based on Euclidean Distance Matrix Completion. In 2016 International Joint Conference on Neural Networks (IJCNN), (pp. 3093-3100). IEEE.
[PDF] [RG]

[C12] Scardapane, S., Scarpiniti, M., Comminiello, D. & Uncini, A. (2016). Diffusion Spline Adaptive Filtering. In 2016 24th European Signal Processing Conference (EUSIPCO), (pp. 1498-1502). Eurasip.
[PDF] [RG] [PRESENTATION] [CODE]

[C11] Scardapane, S., Fierimonte, R., Wang, D., Panella, M. & Uncini, A. (2015). Distributed Music Classification Using Random Vector Functional-Link Nets. In 2015 International Joint Conference on Neural Networks (IJCNN), (pp. 1-8). IEEE.
[PDF] [RG]

[C10] Comminiello D., Scardapane, S., Scarpiniti, M., Parisi, R. & Uncini, A. (2015). Functional Link Expansions for Nonlinear Modeling of Audio and Speech Signals. In 2015 International Joint Conference on Neural Networks (IJCNN), (pp. 1-8). IEEE
[PDF] [RG]

[C9] Scardapane, S., Panella, M., Comminiello, D., & Uncini, A. (2015). Learning from Distributed Data Sources Using Random Vector Functional-Link NetworksProcedia Computer Science, 53, 468-477.
[PDF] [RG] [CODE]

[C8] Scardapane, S., Nocco, G., Comminiello, D., Scarpiniti, M., & Uncini, A. (2014, July). An effective criterion for pruning reservoir’s connections in Echo State Networks. In 2014 International Joint Conference on Neural Networks (IJCNN), (pp. 1205-1212). IEEE.
[PDF] [RG] [PRESENTATION]

[C7] Scardapane, S., Comminiello, D., Scarpiniti, M., & Uncini, A. (2014, July). GP-based kernel evolution for L2-Regularization Networks. In 2014 IEEE Congress on Evolutionary Computation (CEC), (pp. 1674-1681). IEEE.
[PDF] [RG] [PRESENTATION] [CODE]

[C6] Bianchi, F. M., Scardapane, S., Livi, L., Uncini, A., & Rizzi, A. (2014, July). An interpretable graph-based image classifier. In 2014 International Joint Conference on Neural Networks (IJCNN), (pp. 2339-2346). IEEE.
[PDF] [RG]

[C5] Scardapane, S., Comminiello, D., Scarpiniti, M., & Uncini, A. (2013, September). Music classification using extreme learning machines. In 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), (pp. 377-381). IEEE.
[PDF] [RG]

[C4] Comminiello, D., Scardapane, S., Scarpiniti, M., Parisi, R., & Uncini, A. (2013, September). Convex combination of MIMO filters for multichannel acoustic echo cancellation. In 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), (pp. 778-782). IEEE.
[PDF] [RG]

[C3] Comminiello, D., Scardapane, S., Scarpiniti, M., & Uncini, A. (2013, July). Interactive quality enhancement in acoustic echo cancellation. In 2013 36th International Conference on Telecommunications and Signal Processing (TSP),  (pp. 488-492). IEEE.
[PDF] [RG] [PRESENTATION] [CODE]

[C2] Alemanno, A., Travaglini, A., Scardapane, S., Comminiello, D., & Uncini, A. (2013, May). A Framework for Adaptive Real-Time Loudness Control. In Audio Engineering Society Convention 134. Audio Engineering Society.
[PDF] [RG]

[C1] Comminiello, D., Scardapane, S., Scarpiniti, M., & Uncini, A. (2013, May). User-Driven Quality Enhancement for Audio Signal Processing. In Audio Engineering Society Convention 134. Audio Engineering Society.
[PDF] [RG] [PRESENTATION] [CODE]

Book Chapters

[B8] Scardapane, S., Danilo, C., Scarpiniti, M., Parisi, R. & Uncini, A. (2016). Benchmarking Functional Link Expansions for Audio Classification Tasks. In Advances in Neural Networks: Computational Intelligence and ICT (pp. 133-141). Springer International Publishing.
[PDF] [RG] [PRESENTATION]

[B7] Fierimonte, R., Scardapane, S., Panella, M., & Uncini, A. (2016). A Comparison of Consensus Strategies for Distributed Learning of Random Vector Functional-Link Networks. In Advances in Neural Networks: Computational Intelligence and ICT (pp. 143-152). Springer International Publishing.
[PDF] [RG] [PRESENTATION]

[B6] Comminiello, D., Scarpiniti, M., Scardapane, S., Parisi, R., & Uncini, A. (2016). A Nonlinear Acoustic Echo Canceller with Improved Tracking Capabilities. In Recent Advances in Nonlinear Speech Processing (pp. 235-243). Springer International Publishing.

[B5] Scardapane, S., Comminiello, D., Scarpiniti, M., & Uncini, A. (2015). Significance-Based Pruning for Reservoir’s Neurons in Echo State Networks. In Advances in Neural Networks: Computational and Theoretical Issues (pp. 31-38). Springer International Publishing.
[PDF] [RG] [PRESENTATION]

[B4] Comminiello, D., Scardapane, S., Scarpiniti, M., Parisi, R. & Uncini, A. (2015). Online Selection of Functional Links for Nonlinear System Identification. In Advances in Neural Networks: Computational and Theoretical Issues (pp. 39-47). Springer International Publishing.
[PDF] [RG]

[B3] Scardapane, S., Comminiello, D., Scarpiniti, M., & Uncini, A. (2014). A Preliminary Study on Transductive Extreme Learning Machines. In Recent Advances of Neural Network Models and Applications (pp. 25-32). Springer International Publishing.
[PDF] [RG] [PRESENTATION]

[B2] Scarpiniti, M., Comminiello, D., Scardapane, S., Parisi, R., & Uncini, A. (2014). Proportionate Algorithms for Blind Source Separation. In Recent Advances of Neural Network Models and Applications (pp. 99-106). Springer International Publishing.
[PDF] [RG]

[B1] Scardapane, S., Comminiello, D., Scarpiniti, M., Parisi, R., & Uncini, A. (2013). PM10 Forecasting Using Kernel Adaptive Filtering: An Italian Case Study. In Neural Nets and Surroundings (pp. 93-100). Springer Berlin Heidelberg.
[PDF] [RG] [PRESENTATION]